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Record W2160052678 · doi:10.1080/10398560701701239

Intercultural Exchange: An Approach to Training from a Franco-Canadian Perspective

2007· article· en· W2160052678 on OpenAlexaffabout
Roger Parent

Bibliographic record

VenueAustralasian Psychiatry · 2007
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSemioticsSociologyNarrativeSemiotics of cultureIntercultural communicationEpistemologyMeaning (existential)PsychologySocial scienceLinguisticsPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: The current challenges of cultural diversity necessitate effective methods for training professionals in health, as well as other sectors, to work with the phenomenon of culture. This paper presents an overview of a semiotic-based approach to training in this regard. METHODS: Recent publications by Anti Randviir in semiotics on the textual nature of cultural phenomena and by Annabel Levesque on the healthcare issues of Western, French-speaking Canadians provide the methodological frame and basic cultural reference for the overview. RESULTS: The anthropological definition of culture as a 'semiotic', or universe of meaning, offers interdisciplinary common ground for designing practical approaches to cultural analysis, intercultural communication and creativity training. This definition is consistent with convergent findings and research practice in social and cognitive psychology, administrative science, philosophy, ethnography, linguistics and semiotics. CONCLUSIONS: Cultural performances such as narrative constitute an effective methodological tool for interdisciplinary data gathering and for analysis of all kinds of cultures: organizational, family, ethnic, regional, transborder, etc. When combined with a functionalist and systemic approach to the study of culture, semiotic approaches to narrative analysis provide useful principles for decoding cultural modes of communication and for designing meaningful change based on cultural specificity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.367
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2007
Admission routes2
Has abstractyes

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